Visually Secure Deep Joint Source-Channel Coding With Chaotic Map Against Deep Known-Plaintext Attack

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and li...

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Bibliographic Details
Published in:	IEEE open journal of the Communications Society Vol. 6; pp. 1847 - 1858
Main Authors:	Fu, Yuyang, Suto, Katsuya
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Channel coding Chaos theory Chaotic communication Chaotic map encryption Coding Decoding deep joint source-channel coding Deep learning Encoding Encryption Image coding Image communication Image quality Image reconstruction Image transmission Incompatibility known-plaintext attack Receivers Security Signal to noise ratio Tensors visual security
ISSN:	2644-125X, 2644-125X
Online Access:	Get full text
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